Salivary gland organoid culture maintains distinct glandular properties of murine and human major salivary glands.

Salivary glands that produce and secrete saliva, which is essential for lubrication, digestion, immunity, and oral homeostasis, consist of diverse cells. The long-term maintenance of diverse salivary gland cells in organoids remains problematic. Here, we establish long-term murine and human salivary gland organoid cultures. Murine and human salivary gland organoids express gland-specific genes and proteins of acinar, myoepithelial, and duct cells, and exhibit gland functions when stimulated with neurotransmitters. Furthermore, human salivary gland organoids are established from isolated basal or luminal cells, retaining their characteristics. Single-cell RNA sequencing also indicates that human salivary gland organoids contain heterogeneous cell types and replicate glandular diversity. Our protocol also enables the generation of tumoroid cultures from benign and malignant salivary gland tumor types, in which tumor-specific gene signatures are well-conserved. In this study, we provide an experimental platform for the exploration of precision medicine in the era of tissue regeneration and anticancer treatment.

Thermoresponsive fiber-based microwells capable of formation and retrieval of salivary gland stem cell spheroids for the regeneration of irradiation-damaged salivary glands.

Three-dimensional spheroid culture enhances cell-to-cell interactions among stem cells and promotes the expression of stem cell properties; however, subsequent retrieval and delivery of these cells remain a challenge. We fabricated a thermoresponsive fiber-based microwell scaffold by combining electrospinning and hydrogel micropatterning. The resultant scaffold appeared to facilitate the formation of cellular spheroids of uniform size and enabled the expression of more stem cell-secreting growth factor genes (EGF, IGF-1, FGF1, FGF2, and HGF), pluripotent stem cell-related genes (SOX2 and NANOG), and adult epithelial stem cell-related genes (LGR4, LGR5, and LGR6) than salivary gland stem cells in a monolayer culture (SGSCmonolayer). The spheroids could be retrieved efficiently by decreasing temperature. SGSC-derived spheroid (SGSCspheroid) cells were then implanted into the submandibular glands of mice at 2 weeks after fractionated X-ray irradiation at a dose of 7.5 Gy/day. At 16 weeks post-irradiation, restoration of salivary function was detected only in SGSCspheroid-implanted mice. The production of submandibular acini specific mucin increased in SGSCspheroid-implanted mice, compared with PBS control. More MIST1+ mature acinar cells were preserved in the SGSCspheroid-implanted group than in the PBS control group. Intriguingly, SGSCspheroid-implanted mice exhibited greater amelioration of tissue damage and preservation of KRT7+ terminally differentiated luminal ductal cells than SGSCmonolayer-implanted mice. The SGSCspheroid-implanted mice also showed less DNA damage and apoptotic cell death than the SGSCmonolayer-implanted mice at 2 weeks post-implantation. Additionally, a significant increase in Ki67+AQP5+ proliferative acinar cells was noted only in SGSCspheroid-implanted mice. Our results suggest that a thermoresponsive fiber-based scaffold could be of use to facilitate the production of function-enhanced SGSCspheroid cells and their subsequent retrieval and delivery to damaged salivary glands to alleviate radiation-induced apoptotic cell death and promote salivary gland regeneration.

Road Speed Prediction Scheme by Analyzing Road Environment Data.

Road speed is an important indicator of traffic congestion. Therefore, the occurrence of traffic congestion can be reduced by predicting road speed because predicted road speed can be provided to users to distribute traffic. Traffic congestion prediction techniques can provide alternative routes to users in advance to help them avoid traffic jams. In this paper, we propose a machine-learning-based road speed prediction scheme using road environment data analysis. The proposed scheme uses not only the speed data of the target road, but also the speed data of neighboring roads that can affect the speed of the target road. Furthermore, the proposed scheme can accurately predict both the average road speed and rapidly changing road speeds. The proposed scheme uses historical average speed data from the target road organized by the day of the week and hour to reflect the average traffic flow on the road. Additionally, the proposed scheme analyzes speed changes in sections where the road speed changes rapidly to reflect traffic flows. Road speeds may change rapidly as a result of unexpected events such as accidents, disasters, and construction work. The proposed scheme predicts final road speeds by applying historical road speeds and events as weights for road speed prediction. It also considers weather conditions. The proposed scheme uses long short-term memory (LSTM), which is suitable for sequential data learning, as a machine learning algorithm for speed prediction. The proposed scheme can predict road speeds in 30 min by using weather data and speed data from the target and neighboring roads as input data. We demonstrate the capabilities of the proposed scheme through various performance evaluations.

3D Organoid Culture From Adult Salivary Gland Tissues as an ex vivo Modeling of Salivary Gland Morphogenesis.

Lumen formation of salivary glands has been investigated using in vivo or ex vivo rudiment culture models. In this study, we used a three-dimensional (3D) salivary gland organoid culture system and demonstrated that lumen formation could be recapitulated in mouse SMG organoids. In our organoid culture system, lumen formation was induced by vasoactive intestinal peptide and accelerated by treatment with RA. Furthermore, lumen formation was observed in branching duct-like structure when cultured in combination of fibroblast growth factors (FGF) in the presence of retinoic acid (RA). We suggest RA signaling-mediated regulation of VIPR1 and KRT7 as the underlying mechanism for lumen formation, rather than apoptosis in the organoid culture system. Collectively, our results support a fundamental role for RA in lumen formation and demonstrate the feasibility of 3D organoid culture as a tool for studying salivary gland morphogenesis.

User Recommendation for Data Sharing in Social Internet of Things.

As various types of data are generated on the social Internet of things (SIoT), which combine the Internet of things (IoT) and social networks, the relations of IoT devices should be established for necessary data exchange. In this paper, we propose a user recommendation scheme that facilitates data sharing through an analysis of an interaction between an IoT device and a user in the SIoT. An interrelation between a user and an IoT device as well as an interrelation between users exist simultaneously in the SIoT. Hence, the interaction between users must be analyzed to identify the interest keywords, and the interaction between IoT devices and users to determine the user's preference of IoT device. Moreover, the proposed scheme calculates the similarity between users based on the IoT device preference based on IoT device usage frequency and interest keywords, which are identified through an analysis between the user and IoT device and that between users. Subsequently, it recommends top-N users who have a high similarity as the users for data sharing. Furthermore, the performance of the proposed scheme is verified through performance evaluation based on the precision, recall, and F-measure.

Detecting Incremental Frequent Subgraph Patterns in IoT Environments.

As graph stream data are continuously generated in Internet of Things (IoT) environments, many studies on the detection and analysis of changes in graphs have been conducted. In this paper, we propose a method that incrementally detects frequent subgraph patterns by using frequent subgraph pattern information generated in previous sliding window. To reduce the computation cost for subgraph patterns that occur consecutively in a graph stream, the proposed method determines whether subgraph patterns occur within a sliding window. In addition, subgraph patterns that are more meaningful can be detected by recognizing only the patterns that are connected to each other via edges as one pattern. In order to prove the superiority of the proposed method, various performance evaluations were conducted.